Motivated by the constant modulus property of frequency shift keying (FSK) based waveforms and the stabilisation of its radar performance with an increase in the number of subpulses, in this paper an FSK-based dynamic subpulse number joint communications and radar waveform design is proposed. From a communications point of view, the system operates based on traditional FSK modulation. From a sensing point of view, although the subpulses are continuously generated and transmitted, radar waveforms are dynamically formed by monitoring the flatness of the spectrum which in return guarantees the accuracy of the delay estimation. Other constraints on the waveform length are used to ensure satisfactory values of the root mean square time duration, ambiguity function sidelobe levels and prevent overly long waveforms. To provide an estimation of the probability of generating extremely long waveforms, the distribution of the number of subpulses is approximated using a Brownian motion process and an existing result on its one-sided exit density. Numerical examples are provided to evaluate the accuracy of the approximate distribution, as well as the ambiguity function sidelobe levels and the delay and Doppler shift estimation performance of the transmitted waveforms.

Motivated by the ideal peak-to-average-power ratio and radar sensing capability of traditional frequency-coded radar waveforms, this paper considers the frequency shift keying (FSK) based waveform for joint communications and radar (JCR). An analysis of the probability distributions of its ambiguity function (AF) sidelobe levels (SLs) and peak sidelobe level (PSL) is conducted to study the radar sensing capability of random FSK. Numerical results show that the independent frequency modulation introduces uncontrollable AF PSLs. In order to address this problem, the initial phases of waveform sub-pulses are designed by solving a min-max optimisation problem. Numerical results indicate that the optimisation-based phase design can effectively reduce the AF PSL to a level close to well-designed radar waveforms while having no impact on the data rate and the receiver complexity. For large numbers of waveform sub-pulses and modulation orders, the impact on the error probability is also insignificant.

We consider a single-user (SU) continuous fluid antenna system (CFAS) employing matched filtering (MF) operating over a Ricean fading channel. Focusing on the upper tail of the received signal-to-noise ratio (SNR) distribution (the high SNR probability (HSP)), we derive accurate approximations for the HSP in 1, 2, and 3 dimensions using the expected Euler characteristic (EEC), presenting the first analytical results for a CFAS in a Ricean environment. In the process, we provide the first closed-form expression for the Euler characteristic density of a non-central $\chi _2^2$ random field. We then examine the impact of the Ricean K-factor on the CFAS performance, emphasizing the critical role of channel variations in achieving a strong HSP.

Cell-free wireless networks have attracted significant interest for their ability to eliminate cell-edge effects and deliver uniformly high service quality through macro-diversity. In this paper, we develop an algorithm to jointly optimize uplink transmit powers and dynamic user-centric access point (AP) clusters in a centralized cell-free network. This approach aims to efficiently mitigate inter-user interference and achieve higher max-min signal-to-interference-plus-noise ratio (SINR) targets for users. To this end, we re-purpose an iterative power control algorithm based on non-linear Perron-Frobenius theory and prove its convergence for the maximum ratio combiner (MRC) receiver under various AP subset selection schemes. We further provide analytical results by framing the joint optimization as a conditional eigenvalue problem with power and AP association constraints, and leveraging Perron-Frobenius theory on a centrally constructed matrix. The numerical results highlight that optimizing each user’s serving AP cluster is essential to achieving higher max-min SINR targets with the simple MRC receiver.

Recent developments in Multiple-Input-Multiple-Output (MIMO) technology include packing a large number of antenna elements in a compact array to access the bandwidth benefits provided by higher mutual coupling (MC). The resulting super-wideband (SW) systems require a circuit-theoretic framework to handle the MC and channel models which span extremely large bands. Hence, in this paper, we make two key contributions. First, we develop a physically-consistent Rician channel model for use with SW systems. Secondly, we express the circuit-theoretic models in terms of a standard MIMO model, so that insights into the effects of antenna layouts, MC, and bandwidth can be made using standard communication theory. For example, we show the bandwidth widening resulting from the new channel model. In addition, we show that MC distorts line-of-sight paths which has beamforming implications. We also highlight the interaction between spatial correlation and MC and show that tight coupling reduces spatial correlations at low frequencies.

Cell-free wireless networks have garnered significant interest within the research community due to their potential to eliminate cell-edge effects and exploit macro-diversity. In this paper, we design algorithms to jointly optimize uplink transmit powers and dynamic user-centric clusters within a cell-free network. This strategy aims to effectively mitigate inter-user interference and attain spectral efficiency targets for users in a scalable manner. To serve this goal, we re-purpose a classic iterative algorithm and prove its convergence for both the maximum ratio combiner (MRC) and linear minimum mean square error (LMMSE) receivers. We present several access point (AP) subset selection schemes of varying complexity and demonstrate how clustering requirements differ according to receiver capabilities. In particular, we show that optimizing the serving cluster for each user is crucial when using the simple MRC receiver.

Building on recent trends in multiple-input multiple-output and reconfigurable intelligent surfaces, where densely spaced element arrays are considered, this paper focuses on con-tinuous antenna systems where the receive antenna is modelled as a continuous line (in the one-dimensional case) or a continuous surface (in the two-dimensional case). Considering a spatially-correlated Rayleigh process for the communication channel, we conduct an analytical investigation based on matched filtering. More specifically, we derive an approximated distribution for the instantaneous received signal-to-noise ratio (SNR) at the continuous surface. Furthermore, we derive approximations to the achievable rate, average symbol error rate for Mary phase shift keying (MPSK) and an upper bound for the achievable rate. We use extensive numerical examples to illustrate the accuracy of our approximation to the SNR distribution as well as the performance analysis.

Orthogonal time frequency space (OTFS) modulation has recently been identified as a suitable waveform for joint radar and communication systems. Focusing on the effect of data modulation on the radar sensing performance, we derive the ambiguity function (AF) of the OTFS waveform and characterize the radar global accuracy. We evaluate the behavior of the AF with respect to the distribution of the modulated data and derive an accurate approximation for the mean and variance of the AF, thus, approximating its distribution by a Rice distribution. Finally, we evaluate the global radar performance of the OTFS waveform with the OFDM waveform.

This paper focuses on waveform design for joint radar and communication systems and presents a new subset selection process to improve the communication error rate performance and global accuracy of radar sensing of the permutation based random stepped frequency radar waveform. An optimal communication receiver based on integer programming is proposed to handle any subset of permutations followed by a more efficient sub-optimal receiver based on the Hungarian algorithm. Considering optimal maximum likelihood detection, the block error rate is analyzed under both additive white Gaussian noise and correlated Rician fading. We propose two methods to select a permutation subset with an improved block error rate and an efficient encoding scheme to map the information symbols to selected permutations under these subsets. From the radar perspective, the ambiguity function is analyzed with regards to the local and the global accuracy of target detection. Furthermore, a subset selection method to reduce peak-to-sidelobe ratio (PSLR) is proposed by extending the properties of Costas arrays. Finally, the process of remapping the frequency tones to the symbol set used to generate permutations is introduced as a method to improve both the communication and radar performances of the selected permutation subset.

This paper derives the optimum detection rule for communication systems with n-bit phase quantization when data is differentially encoded at the transmitter. The proposed approach avoids the channel estimation problem at the receiver. First, a maximum likelihood detection rule for block-2 detectors utilizing only two consecutive quantized observations at the channel output is obtained. Second, it is shown that the derived maximum likelihood detection rule continues to be optimum for the class of block-L detectors for L ≥ 3 when n = log2M, where M is the input alphabet size. Finally, utilizing the structure of the derived optimum detector, a message error probability expression is obtained for Rayleigh fading wireless channels. A simulation study is performed to illustrate the performance of the optimum detectors as well as the performance loss due to the lack of channel knowledge at the receiver. The proposed approach and the solutions presented in this paper provide an initial step to communicate with low-resolution ADCs without requiring receiver-side channel knowledge.

Distributed massive multiple-input multiple-output (mMIMO) is a key technology for improving the performance of future wireless communication systems. As an alternative to channel estimation in mMIMO systems, non-coherent detection offers several advantages. In this paper, we present a comprehensive analysis of non-coherent detection for distributed mMIMO systems. We obtain novel expressions for the signal-to-interference-and-noise ratio (SINR) for differential detection. Building upon a theoretical basis, we show that under non-coherent detection, cooperation is always beneficial in noise-limited conditions. We further investigate scenarios where the gains of cooperation are most evident. We then obtain useful error rate results by deriving expressions for the symbol error probability (SEP) when using differential detection. The results are illustrated by numerical examples and simulations.

Cell-free wireless networks have gained the interest of the research community in recent years due to their potential to provide good quality of service (QoS) to all users. However, this comes at the cost of complex signal processing and high computational demands from the network to actively suppress inter-user interference. In this paper, we undertake joint optimization of the uplink power and the dynamic clusters in a cell-free wireless network to achieve spectral efficiency targets for users in a scalable and efficient manner. To this end, we re-purpose a classic iterative algorithm and theoretically prove its convergence to our objectives. We introduce various base station (BS) subset selection schemes and study their performance, establishing a trade-off between the performance and computational cost of the algorithm while facilitating a distributed operation. The numerical results convey the favorable impact of allowing variable cluster size in a system that employs maximal ratio combining (MRC).

In this paper, we consider an underlay cognitive radio network assisted by dual-hop decode-and-forward (DF) relaying. For a general multi-user network, we adopt a max-min fairness relay selection scheme and analyse the outage probability when the channels are subject to independent and non-identical Nakagami-m fading. The relay network operates within the constraint imposed on the peak interference power tolerable by the primary receiver. We then analyse the asymptotic outage probability performance and illustrate the existence of i) the full-diversity order when the interference level at the primary user increases proportionally with the relay transmit power; and ii) an outage floor when the transmit powers of the relays are restricted by the primary receiver. We also analyse the outage probability with imperfect channel state information (CSI) and the average throughput over Rayleigh fading channels. Illustrative analytical results are accurately validated by numerical simulations.

We propose a new partial joint processing architecture for distributed massive multiple-input multiple-output (MIMO) networks. As opposed to the traditional full-joint processing architecture, where the channel coefficients of all the users within the cooperating cluster are learnt at the base stations, in the proposed architecture, we allow each base station to learn the channel coefficients only of the users that maintain a strong average received signal-to-noise ratio to that base station based on a predefined threshold. This threshold provides extra flexibility, trading-off channel estimation for performance. We assume a zero-forcing receiver at the central processing unit using estimated channels and unknown terms are set to zero. We then derive an accurate approximation for the instantaneous received signal-to-interference-and-noise ratio of an arbitrary user. We use this approximation to derive closed-form expressions for the achievable rate and symbol error probability of an arbitrary user. Numerical examples are used to illustrate the accuracy of the analysis.

In this paper, we focus on the sum-rate optimization problem in a general dual-hop relay network by considering the joint relay selection and power control in the presence of interference. First, we propose a new relay selection algorithm which has better sum-rate performance than the existing relay selection techniques. Then we combine relay selection and power control to propose a novel iterative algorithm based on the tight lower bound approximation which maximizes the achievable sum-rate. We also prove that for the special case of two-user networks, binary power allocation is optimum for at least two transmitting nodes. Extensive numerical examples are used to compare the performance of the proposed algorithm and to illustrate the accuracy of the analysis.

In this paper, we focus on the problem of transmitter and receiver scheduling to maximize the achievable sum-rate of a flexible half-duplex network where nodes have the flexibility to either transmit, receive or be silent in a given time slot. We consider a network with multiple transmitters and receivers where each transmitter has specific information it needs to send to a set of receiving nodes. First, we conduct some structural analysis and show that the achievable sum-rate is maximized when each transmitter only transmits to a single receiver at a given time. Next, we consider one instance of the flexible network and by reducing the symmetric multiple receiver network to a single receiver network, we also show that the achievable sum-rate is maximized when either one transmitter or all the transmitters transmit. In fact, there exists a unique received signal-to-noise ratio at which the optimality changes from all-to-one. Finally, we design a novel low-cost algorithm that gives a sub-optimal solution to the achievable sum-rate maximization problem in a flexible half-duplex network. We also provide a comprehensive comparison of the proposed algorithm with respect to existing resource allocation techniques, and observe that our proposed algorithm provides significant sum-rate gains.

This paper presents a new joint radar and communication technique based on the classical stepped frequency radar waveform. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. A new signaling scheme is proposed in which the mapping between incoming data and waveforms is performed based on an efficient combinatorial transform called the Lehmer code. Considering the optimum maximum likelihood detection, the union bound and the nearest neighbour approximation on the communication block error probability is derived for communication in an additive white Gaussian noise channel. The results are further extended to incorporate the Rician fading channel model, of which the Rayleigh fading channel is presented as a special case. Furthermore, an efficient communication receiver implementation is discussed based on the Hungarian algorithm which achieves optimum performance with much less operational complexity when compared to an exhaustive search. From the radar perspective, two key analytical tools, namely, the ambiguity function and the Fisher information matrix are derived. Furthermore, accurate approximations to the Cramer-Rao lower bounds on the delay and Doppler estimation errors are derived based on which the range and velocity estimation accuracy of the waveform is analysed.

In this paper, we investigate antenna selection strategies for multi-antenna wireless communication systems with low-resolution quantizers. We propose three sub-optimum but low-complexity antenna selection strategies and characterize their symbol error probability performance. We show that the strategy that selects the diversity branch with channel rotated constellation points being furthest away from the decision boundary is the best strategy in terms of symbol error probability. Using numerical analysis, we provide evidence to suggest that this selection strategy achieves the same diversity order as the optimum maximum likelihood (ML) detector under the same operating conditions. An extensive simulation study is performed to illustrate the accuracy of the derived results.

This paper presents a new waveform that is suitable for simultaneous data transmission and radar sensing. The approach considers a classical random stepped frequency radar waveform that is suitable for the emerging automotive radar application. The randomization in the waveform, which is achieved by using permutations of the sequence of frequency tones, is utilized for data transmission. More specifically, we propose a new Lehmer code based signaling model that modulates data based on the selection of the permutation. Considering maximum likelihood detection, the union bound on the communication block error probability is derived for baseband communication both in an additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Using the Hungarian Algorithm, an efficient implementation method for the communications receiver is also presented. From the radar perspective, we derive the ambiguity function, which is a key analytical tool in radar waveform design, and characterize the behavior of the Lehmer code based random stepped frequency radar waveform. Numerical examples are used to illustrate the performance of the proposed waveform.

In this paper, we focus on maximization of the instantaneous sum-rate in flexible half-duplex networks, where nodes have the flexibility to choose to either transmit, receive or be silent in a given time slot. Since the corresponding optimization problem is NP-hard, we design low-cost algorithms that give sub-optimal solutions with good performance. We first consider two existing approximation techniques to simplify the sum-rate optimization problem: arithmetic-geometric means inequality and another utilising the tight lower bound approximation. We then propose a novel pattern search algorithm that performs close to exhaustive search but with significantly lower complexity. Comparing the performance of the proposed algorithm with respect to existing resource allocation techniques, we observe that our proposed algorithm provides significant sum-rate gains.

In this paper, we analyse the optimality of binary power allocation in a network that includes full-duplex communication links. Considering a network with four communicating nodes, two of them operating in half-duplex mode and the other two in full-duplex mode, we prove that binary power allocation is optimum for the full-duplex nodes when maximizing the sum rate. We also prove that, for half-duplex nodes binary power allocation is not optimum in general. However, for the two special cases, 1) the low signal-to-noise-plus-interference (SINR) regime and, 2) the approximation by the arithmetic mean-geometric mean inequality, binary power allocation is optimum for the approximated sum rate even for the half-duplex nodes. We further analyse a third special case using a symmetric network for which the optimum power allocation is binary, under a sufficient condition. Numerical examples are included to illustrate the accuracy of the results.

The energy detector and the sphericity test are two widely used spectrum sensing techniques that utilize different properties of the signal received at the secondary user terminal. In this paper we use meta analysis to combine these two techniques and derive two novel mixture detectors that outperform both techniques. Since the spectrum sensing capability of the energy detector is limited by the uncertain knowledge of the noise power, first, we analyze the performance of the energy detector with estimated noise power. We derive analytical expressions for the false alarm and the detection probabilities when the secondary user terminal is equipped with multiple antennas. Next, we apply meta analysis to combine the outputs of the energy detector and the sphericity test to derive two mixture detectors, namely, Fisher's method and the weighted z-transform method. Furthermore, we extend our analysis to consider cooperative spectrum sensing where multiple secondary user terminals cooperatively detect the presence of primary users. Based on the mixture detectors, we propose two new cooperative spectrum sensing techniques and derive simple analytical expressions for false alarm probabilities. Extensive numerical examples are used to illustrate the accuracy of our analysis and to highlight the performance gains obtained by the mixture detectors.

This paper considers a low-resolution wireless communication system in which transmitted signals are corrupted by fading and additive noise. First, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. Second, in the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equi-probable signal points is derived. Third, utilizing the structure of the derived optimum receiver, a general average SEP expression for the M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Finally, an extensive simulation study of the derived analytical results is presented for general Nakagami-m fading channels. It is observed that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n ≥ log2M + 1. That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n ≥ log2M + 1. On the other hand, it is only equal to 1/2 and 0 for n = log2M and n ≥ log2M, respectively. Hence, for fading environments with a large value of m, using an extra quantization bit improves communication reliability significantly.

We consider spectrum sensing in a cognitive radio network with arbitrary numbers of primary and secondary users. Based on the sphericity test, we first analyze the centralized spectrum sensing where all the data available at the secondary users are combined for the signal detection of primary users. We derive accurate approximations for the false alarm and detection probabilities that are also compared against the approximations already available in the literature. Next, we analyze the distributed spectrum sensing where only partial data from each secondary user are used in the signal detection of primary users. Two novel techniques, namely, the multisample sphericity test and metaanalysis, are proposed and analyzed. Instead of sending all the raw data received at the secondary user terminals, in the multisample sphericity test and metaanalysis, only one or two real numbers are required to be sent to a central processor to make a decision about the presence of primary users. Accurate analytical expressions on the false alarm and detection probabilities are derived, and numerical examples are provided to verify their accuracy. Receiver operating characteristic curves are also presented to compare the performance of the proposed methods.

In this paper, we present a new theoretical analysis of the performance of a distributed antenna system (DAS) with a constraint on the number of RF chains. We consider a cooperating network model with receiver sites distributed across the network. Each receive site is equipped with a limited number of RF chains that picks the best set of antennas based on the channel between the transmitter and the receiver. Considering a hybrid generalized selection/maximal ratio combining (GS/MRC) scheme, we derive new expressions for three important performance measures, namely, the achievable rate, symbol error probability (SEP) and outage probability. Our expressions are based on closed-form expressions we derive for the moment generating function (MGF) of the received signal-to-noise-ratio (SNR). We further analyse the high SNR error performance to accurately characterize the diversity order and the array gain of the system. Numerical examples demonstrate that our analytical results accurately reflect the simulations for a wide range of network scenarios. We highlight that the DAS with GS/MRC requires fewer RF chains and introduces a lower hardware complexity when compared to conventional MRC.

We consider distributed spectrum sensing in cognitive radio networks with multiple primary and secondary user terminals. Two novel techniques based on the sphericity test, namely, the multisample sphericity test and meta analysis, are analysed in such a scenario. Instead of sending all the raw data received at the secondary user terminals, as in the case with centralized spectrum sensing, in the multisample sphericity and meta analysis tests only one or two real numbers are required to be sent to the central processor to make a decision about the presence of primary users. Accurate analytical expressions on the false alarm probability are derived for both techniques and numerical examples are provided to verify their accuracy. Receiver operating characteristic (ROC) curves are also presented to compare the performance of the proposed methods and other simple fusion techniques.

This paper analyzes the performance of a multi-user multihop relay network using a low complexity decentralized relay selection (DRS) scheme for decode-and-forward cooperative networks. We carry out a rigorous diversity order analysis, with Nakagami-m fading and pathloss and show that the DRS scheme achieves full diversity while maintaining a complexity that is quadratic in the number of users, quadratic in the number of relays and independent of the number of hops. For a special case of two-user networks we derive exact closed-form expressions for the outage probability by considering the order statistics. Furthermore, we extend our analysis to consider interfering relay networks and derive an accurate lower bound on the outage of an arbitrary network user. Based on the lower bound we also show how the outage probability saturates in the high signal-to-interference-plus-noise ratio regime. Extensive numerical examples are used to illustrate the accuracy of the analysis and to highlight the use of the DRS scheme in multi-user multihop relay networks.

In this paper, we analyze the outage and diversity performance of a low-complexity relay selection routing algorithm which applies to large-scale distributed decode-and-forward relay networks with two source-destination user pairs. We analyze a suboptimal decentralized relay selection (DRS) strategy that only utilizes local channel state information of the relays within a given hop, to select distinct multihop paths for each user pair. Specifically, we derive exact closed-form expressions for the outage probability and diversity order of the DRS algorithm which prove that the full diversity order is achieved with complexity that is quadratic with the number of relays in each hop. Illustrative analytical results are accurately validated by numerical simulations.

Reconfigurable antenna arrays provide a means for efficient use of the spatial domain in wireless communication systems. Despite its potential, the topic is only briefly explored in the literature. In this paper, we present a comprehensive theoretical analysis of the performance of reconfigurable systems. We consider a receiver equipped with multiple reconfigurable antennas that pick the best state based on the channel between the transmitter and the receiver. For such a system, we derive a new expression for the moment generating function (MGF) of the received signal-to-noise ratio by employing maximal ratio combining. Based on the MGF, we analyze three important performance measures, specifically, achievable rate, error probability, and outage probability. Furthermore, we conduct an asymptotic analysis incorporating the correlation between reconfigurable states and show that a reconfigurable system can achieve a diversity order of the number of antennas times the number of reconfigurable states. Finally, we discuss the applicability of reconfigurable antennas in novel wireless networks with large antenna arrays and distributed antenna systems, highlighting the performance gains and requirement for fewer RF chains.

This paper analyzes the symbol error probability (SEP) of cluster-based cooperative networks where a finite cluster of base stations jointly detect multiple in-cluster users in the presence of out- of-cluster interference. For such a network, we derive new accurate upper and lower bounds on the SEP of the in-cluster users with Rayleigh fading, arbitrary path loss, and M-ary phase-shift keying modulation. We further derive new asymptotic expressions to accurately characterize the SEP saturation limit arising from out-of-cluster interference. To obtain deeper insights, we extend our analysis to consider Rician fading with line- of-sight paths from the in-cluster users to their nearest base stations. Numerical examples illustrate the accuracy of our results and highlight novel aspects of fading channels, path loss, cluster configurations, and user locations on the SEP of cluster-based cooperative cellular networks.

We consider cellular networks with co-operative clusters of neighboring base stations detecting multiple in-cluster users subject to interference from out-of-cluster users. We assume that the base stations, equipped with multiple antennas, are connected to a central processor in each cluster. For such a network, we first consider centralized processing where all the in-cluster user signals are sent to the central processor for linear minimum mean squared error (LMMSE) estimation. Next, we consider partially decentralized processing where the in-cluster user signals are locally estimated at each base station, and the local estimates are combined at the central processor. For both processing architectures, we derive new expressions for the achievable rate of an in-cluster user when the channels between the users and base stations are subject to independent Rayleigh fading and distance-dependent path loss. The solutions are based on accurate approximations we derive for the characteristic function (CF) and the probability density function (PDF) of each user's signal-to-interference-plus-noise ratios (SINRs). Numerical examples highlight the accuracy of the analysis and compare the performance of centralized and partially decentralized processing under different cluster scenarios.

We examine the sum capacity of a cluster-based cooperative cellular network where a linear minimum-mean squared error (LMMSE) estimator is deployed across a cluster of base stations to estimate multiple in-cluster users. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive the sum capacity of the in-cluster users with independent Rayleigh fading and arbitrary path loss. The sum capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the in-cluster users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our new analytical expressions accurately characterize the impact of out-of-cluster interference and cluster size on the sum capacity. We observe that out-ofcluster interference results in a sum capacity saturation regime when the transmit power is large. We also illustrate that the saturation threshold increases with the cluster size. Furthermore, we examine the sum capacity under different path loss exponents which highlights the significance of out-of-cluster interference.

We consider a cooperative cellular network with multiple users transmitting within a cooperative cluster of multiple antenna base stations. Different from global cooperation where all the signal processing is performed at a central processor, we analyze a distributed processing architecture that performs the estimation of user symbols in two steps, namely, 1) Local LMMSE estimation of the user symbols at each base station, and 2) Central combining of all the estimates from the base stations. For such a network, we derive new expressions for the capacity of a given user with independent Rayleigh fading and arbitrary path loss between the users and all the antennas at the base stations. Our capacity expression is based on accurate approximations we derive for the characteristic function and the probability density function of the users' signal-to-interference-plus-noise ratios (SINRs). Numerical examples demonstrate that our analytical solutions accurately approximate the exact capacity. Furthermore, we highlight that the distributed approach introduces less overhead to the network compared with global cooperation. We note that the performance gap is small when the network is lightly loaded.

We consider a multi-cell cooperative network where a cluster of base stations jointly detect the signals from multiple users transmitting within the cluster. Different from previous works, we examine the impact of interference from out-of-cluster users whose transmit power scales with that of the in-cluster users. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) of the in-cluster users with independent Rayleigh fading and arbitrary path loss. We observe that our lower bound accurately approximates the BEP at low signal-to-noise ratios (SNRs), whereas the upper bound is accurate at high SNRs. Our analytical bounds accurately characterize the impact of out-of-cluster interference and cluster size on the BEP. Specifically, we highlight that out-of-cluster interference results in a BEP saturation regime when the transmit power is large. We also show that the saturation threshold increases with the cluster size.

We present new analytical expressions for optimal multiuser detection in the uplink of a cellular network with base station cooperation. We consider a cooperative multicell scenario where multiple base stations jointly detect the signals from multiple users distributed throughout the network. For such a network, we derive new upper and lower bounds on the uncoded bit error probability (BEP) with independent Rayleigh fading and arbitrary path loss between the users and the base stations. Our analytical results are further simplified to produce closed-form bounds on the BEP when the path loss from a given user to each base station is distinct. We demonstrate that the lower bound is accurate at low signal-to-noise ratios (SNRs) while the upper bound is accurate at medium to high SNRs.